Systems, methods and computer program products for modeling a monetary measure for a good based upon technology maturity levels

ABSTRACT

A systems, methods and computer program products are provided for modeling a monetary measure of a good, such as a cost or revenue associated with the good. A method begins by selecting at least one qualitative measure of maturity for at least one technology associated with the good, where each qualitative measure of maturity is associated with a distribution such that each technology is correspondingly associated with a distribution. Next, a monetary point is associated with each technology, and thereafter a monetary distribution is determined for each technology based upon a respective monetary point and a respective distribution. A plurality of monetary values are selected by randomly selecting the plurality of monetary values for each technology based upon a respective monetary distribution. Finally, the monetary measure for the good are modeled based upon the selected monetary values for each technology.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.10/453,395, entitled: Systems, Methods and Computer Program Products forModeling a Monetary Measure for a Good Based Upon Technology MaturityLevels, filed Jun. 3, 2003, now U.S. Pat. No. 7,627,494 which is herebyincorporated herein in its entirety by reference.

FIELD OF THE INVENTION

The present invention generally relates to systems, methods and computerprogram products for modeling cost and associated revenue associatedwith a project for the manufacture and sale of a good and, moreparticularly, relates to systems, methods and computer program productsfor modeling cost and associated revenue associated with a project basedupon at least one technology maturity level.

BACKGROUND OF THE INVENTION

In many industries, decisions about projects for the manufacture andsale of a good require manufacturers to estimate technical risk, ortechnical maturity, associated with the state of development of theproject in order to correctly determine success probabilities andinvestment levels for the project. In this regard, development of theproject can include one or more different technologies, with differenttechnologies in different stages of development. For example, a projectcan include a technology associated with materials utilized tomanufacture the good that is in one stage of development, and atechnology associated a particular manufacturing process that isutilized during the production of the good that is in another stage ofdevelopment.

Whereas information regarding technical risk can be useful tomanufacturers, such information is often qualitative. As such, it isoften difficult to ascertain the impact of risk on the success of theproject. For example, one such group of qualitative measures oftechnical risk, or technical maturity, are the Technology ReadinessLevels (TRL's) developed by the National Aeronautics and SpaceAdministration (NASA). As shown in the Table, below, the NASA TRL'srepresent a qualitative measure of the state of development of theproject. As shown, the NASA TRL's consist of nine levels representingvarious stages of development. More particularly, TRL 1 corresponds tothe lowest level of technology maturation. From TRL 1, then, the levelsof technology increase through TRL 9, which corresponds to actualsuccessful use of a fully developed project in an applicable environmentfor the project. At TRL 9, then, development of the project hastypically matured past the point of troubleshooting problems discoveredduring testing, which typically occur at TRL 8.

NASA Technology Readiness Level (TRL) TRL 1 Basic Principles Observedand Reported TRL 2 Technology Concept and/or Application Formulated TRL3 Analytical and Experimental Critical Function and/or CharacteristicProof-of-Concept TRL 4 Component and/or Breadboard Validation inLaboratory Environment TRL 5 Component and/or Breadboard Validation inRelevant Environment TRL 6 System/Subsystem Model or PrototypeDemonstration in a Relevant Environment TRL 7 System PrototypeDemonstration in a Space Environment TRL 8 Actual System Completed and“Flight Qualified” Through Test and Demonstration TRL 9 Actual System“Flight Proven” Through Successful Mission Operations

As will be appreciated, the state of development of the technologiesassociated with a project can have a direct impact on the uncertainty,and as such the risk, associated with the cost and/or revenues ofmanufacturing and selling the good. In this regard, the ability tocorrectly estimate the technical risk impact on a project enablesmanufacturers to make better decisions as to investment and proportionof the investment to the level of project risk. Conventionally,manufacturers have not had the ability to reliably quantify thetechnical risk of a project proposal that has limited information, asextensive uncertainties, is concerned with new markets or technologies,or does not yet have a well-defined scope or specifications.

SUMMARY OF THE INVENTION

In light of the foregoing background, the present invention providessystems, methods and computer program products for modeling a monetarymeasure of a good, such as a cost and/or revenue associated with a good,where the model is based upon at least one, and more typically aplurality, of technology maturity levels. The systems, methods andcomputer program products of embodiments of the present invention arecapable of modeling quantitative risk/return as a function ofqualitative measures of maturity to thereby reliably quantify thetechnical risk of a good based upon the level of maturity of thetechnologies associated with the good. Advantageously, embodiments ofthe present invention are capable of modeling the quantitativerisk/return for technologies at a number of different levels ofdevelopment, where each level of development has an associated technicalrisk, or uncertainty. In addition, embodiments of the present inventionare capable of modeling quantitative risk/return in a robust manner tomodel monetary measures in a number of different contexts, withoutmodifying the model to fit a particular monetary measure, good, projector the like. Embodiments of the present invention are thus capable ofeasily assigning accurate quantitative assessments of risk and returnthrough the extraction of distribution statistical parameterscharacteristic of representative capital and industry markets. Further,embodiments of the present invention are capable of enabling accurate,rapid and standardized characterization of cost and revenueuncertainties, and enabling rapid prototyping of business casesimulations that apply techniques such as the Monte Carlo techniques.

According to one aspect of the present invention, systems, methods andcomputer program products are provided for modeling quantitativerisk/return as a function of a plurality of qualitative measures ofmaturity, such as technology readiness levels. The method of oneembodiment begins by determining a risk/return distribution for each ofa plurality of risk values, where the risk values can be ordered from alowest risk value to a highest risk value. After determining therisk/return distributions, a qualitative measure of maturity is assignedto each of the plurality of risk/return distributions based upon arespective risk value to thereby model quantitative risk/return as afunction of the plurality of measures of maturity. Similar to the riskvalues, the measures of maturity can be ordered from a highest measureof maturity to a lowest measure of maturity. As such, the measures ofmaturity can be assigned by assigning a higher measure of maturity to arisk/return distribution for a lower risk value.

Advantageously, a risk/return distribution can be determined for each ofa plurality of risk values based upon a log mean and a log standarddeviation of a lognormal probability density reflecting a respectiverisk and return pattern of capital markets, such as according to theCapital Asset Pricing Model (CAPM). As such, each risk/returndistribution typically comprises a lognormal distribution. In variousembodiments, each of the risk/return distributions can be normalized toa mode of one. Additionally, or alternatively, an upper and lower boundof each of the risk/return distributions can be truncated by apredefined percentage. In other embodiments, alternatively or inaddition to normalizing and truncating the risk/return distributions,each lognormal risk/return distribution can be converted to acorresponding triangular distribution. Also in such embodiments,assigning a qualitative measure can include assigning a qualitativemeasure of maturity to each triangular distribution.

According to another aspect of the present invention, a systems, methodsand computer program products are provided for modeling a monetarymeasure of a good, such as a cost or revenue associated with the good.The method of one embodiment begins by selecting at least onequalitative measure of maturity for at least one technology associatedwith the good. In this regard, each qualitative measure of maturity isassociated with a distribution such that each technology is associatedwith the distribution of the respective qualitative measure of maturity.As such, the qualitative measures of maturity can be selected from amodel of quantitative risk/return, as such may be determined asdescribed above.

Next, a monetary point, such as a most likely cost or revenue value, isassociated with each technology. A monetary distribution is thendetermined for each technology based upon the respective monetary pointand a respective distribution. A plurality of monetary values areselected by randomly selecting, such as according to the Monte Carlotechnique, the plurality of monetary values for each technology basedupon the respective monetary distribution. Finally, the monetary measurefor the good is modeled based upon the selected monetary values for eachtechnology.

Therefore, the systems, methods and computer program products ofembodiments of the present invention are capable of modeling a monetarymeasure of a good, such as a cost and/or revenue associated with a good,based upon technology maturity levels. In this regard, embodiments ofthe present invention can model quantitative risk/return as a functionof qualitative measures of maturity to thereby reliably quantify thetechnical risk of a good based upon the level of maturity of thetechnologies associated with the good. Advantageously, embodiments ofthe present invention are capable of modeling the quantitativerisk/return for technologies at a number of different levels ofdevelopment, where each level of development has an associated technicalrisk, or uncertainty. In addition, embodiments of the present inventionare capable of modeling quantitative risk/return in a robust manner suchthat monetary measures can be modeled in a number of different contexts,without modifying the model to fit a particular monetary measure, good,project or the like.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 is a flow chart illustrating various steps in a method ofmodeling risk/return as a function of measures of maturity according toone embodiment of the present invention;

FIG. 2 is an illustration of a risk/return distribution and acorresponding triangular distribution according to one embodiment of thepresent invention;

FIG. 3 is a flow chart illustrating various steps in a method ofmodeling a monetary measure of a good according to one embodiment of thepresent invention; and

FIG. 4 is a schematic block diagram of the system of one embodiment ofthe present invention embodied by a computer.

DETAILED DESCRIPTION OF THE INVENTION

The present invention now will be described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsof the invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art. Likenumbers refer to like elements throughout.

Referring to FIG. 1, a method of modeling quantitative risk/return as afunction of at least one qualitative measure of maturity is shownaccording to one embodiment of the present invention. As shown, themethod of this embodiment can begin by modeling returns as a function ofrisk, or uncertainty, associated with bringing a good to market. Returnscan be modeled in any of a number of different manners, such as byestimators or the like. In one embodiment, for example, the returns aremodeled from two return values and associated risk values, as such maybe determined by an estimator. Then, assuming an approximately linearrelationship between risk and return, the returns can be modeled as alinear function of risk based upon the two return values and associatedrisk values. For example, according to one embodiment, two return valuesmay comprise 10.0% and 12.5%, with associated risk values comprising 20%and 30%, respectively. With such values, returns can be modeled as alinear function of risk as follows:Return(Risk)=0.25×Risk+5where return and risk are expressed as percentages. As will beappreciated, in addition to utilizing the return model as describedbelow, the return model can be utilized to model future revenues of agood, where the future revenues are subject to an amount of uncertainty.For a description of such an application, see U.S. patent applicationSer. No. 10/453,396, entitled: Systems, Methods and Computer ProgramProducts for Modeling Uncertain Future Benefits, filed Jun. 3, 2003, thecontents of which is incorporated by reference in its entirety.

Either before, after or as returns are modeled as a function of risk,risk/return distributions are determined for different risk values, asshown in block 12. The risk/return distributions can be determined forany number of different risk values but, in one embodiment, the numberof different risk values corresponds to the number of differentqualitative measures of maturity, as described below. In this regard,the number of different qualitative measures of maturity are typicallypredefined. For example, when the number of qualitative measures ofmaturity equals nine, the number of different risk, or uncertainty,values can comprise those shown in Table 1, along with risk/returndistribution can also be defined by a percentage standard deviation ofthe lognormal probability density reflecting a risk and return patternof capital markets. By determining the risk/return distributions basedupon the risk and return patterns of capital markets, the risk/returndistributions can be robustly applied to any of a number of differentmonetary measures without having to be modified to fit a particularmonetary measure, good, project or the like.

As shown in block 12, after determining the risk/return distributions,each risk/return distribution can be normalized to a mode of one, asillustrated in block 14. It will be appreciated by those skilled in theart that at the highest bounds of possible risk/return, goods aretypically dropped or re-scoped into different goods. Similarly,

TABLE 1 Growth Rate Uncertainty 10.0% 20% 12.5% 30% 15.0% 40% 17.5% 50%20.0% 60% 22.5% 70% 25.0% 80% 27.5% 90% 30.0% 100% at the lowest bounds of possible risk/return, several factors, such ascompetitors entering the market and reaching capacity of the market, actto limit the return. In this regard, whereas the risk/returndistributions can be determined to define the entire range of possiblerisk/return, in one embodiment the lowest and highest bounds of eachrisk/return distribution are truncated, as shown in block 16. Forexample, the each bound can be truncated by a predefined percentage(e.g., 2.5%) so that the risk/return distributions define the rage ofmost likely risk/return. According to one exemplar embodiment, then, therisk/return distributions can be summarized for a number of differentrisk values, as shown below in Table 2.

More particularly, the lognormal standard deviation (LogStdDev),lognormal mean (LogMean), mode (Mode) and % values (97.5% and 2.5%values) shown in Table 2 can be determined according to the followingequations:

${LogMean} = {{- \frac{1}{2}} \times {\ln( {( {( {{StdDev}/{Mean}} )^{2} + 1} )/{Mean}^{2}} )}}$${LogStdDev} = \sqrt{\ln( {( {{StdDev}/{Mean}} )^{2} + 1} )}$Mode = 𝕖^(LogMean − LogStdDev²)%  value = 𝕖^(LogMean + LogStdDev × Normslnv(percent))For more information on such a method of determining the precedingvalues, see U.S. patent application Ser. No. 10/453,396, entitled:Systems, Methods and Computer Program Products for Modeling UncertainFuture Benefits.

As shown, Table 2 includes the mean value for each risk/returndistribution. As will be appreciated by those skilled in the art, themean value cannot be directly determined but, instead, may be determinedas that value that uniquely satisfies a lognormal distribution that hastwo constraints: (1) a standard deviation of X %, and (2) a mode valueequal to one. More particularly, as can be seen from the previousequations, if the standard deviation is known, but the mean value is notknown (as are the lognormal mean and lognormal standard deviationvalues), solving for the mean value is circular. As such, the only wayto solve for the mean value is by utilizing a search algorithm withconstraints.

After defining the risk/return distributions, or after normalizingand/or truncating the risk/return distributions, the risk/returndistributions can, but need not, be converted to roughly equivalenttriangular distributions, as shown in block 18. By converting therisk/return distributions to triangular distributions, subsequentanalysis can be performed on more simplified triangular distributions,as opposed to lognormal distributions. And because the bounds of therisk/return distributions have been truncated, the risk/returndistributions can be converted to triangular distributions withoutsacrificing much accuracy during the conversion. For an example of arisk/return distribution and a corresponding triangular distribution,see FIG. 2. It should be understood, however, that the risk/returndistributions need not be converted to triangular distributions topractice the present invention.

The risk/return distributions can be converted to triangulardistributions according to any of a number of different techniques.According to one technique, for

TABLE 2 StdDev. Dist. LogStdDev. LogMean 97.5% 2.5% Mode Mean 20% 0.190.04 1.50 0.72 1.00 1.06 30% 0.27 0.07 1.80 0.64 1.00 1.11 40% 0.33 0.112.13 0.58 1.00 1.18 50% 0.39 0.15 2.47 0.55 1.00 1.25 60% 0.43 0.19 2.610.52 1.00 1.32 70% 0.47 0.22 3.16 0.50 1.00 1.40 80% 0.51 0.26 3.51 0.481.00 1.47 90% 0.54 0.29 3.85 0.46 1.00 1.55 100%  0.67 0.32 4.20 0.451.00 1.62example, the minimum and maximum values of each triangular distributioncan be selected to equal the truncated lower and upper bounds,respectively, of a respective risk/return distribution. Then, like therisk/return distributions, the triangular distribution can be normalizedto a mode equal to one. Therefore, continuing the example of Table 2above, the triangular distributions associated with respectiverisk/return distributions of Table 2 can be summarized as in Table 3,below. As will be appreciated by those skilled in the art, neither therisk/return distributions nor the triangular distributions need benormalized to a mode of one. In this regard, as described below, apoint, or most likely, cost or revenue can be mapped onto a respectivetriangular distribution that has not been normalized. In such aninstance, the respective triangular distribution can have a mode notequal to one, which can then be accounted for when mapping the mostlikely cost or revenue onto the respective triangular distribution, assuch is known to those skilled in the art.

After converting the risk/return distributions to correspondingtriangular distributions, the triangular distributions can be assignedto different qualitative measures of maturity to thereby modelrisk/return as a function of measures of maturity, as shown in block 20.The qualitative measures of maturity can include any number of differentmeasures that typically describe a number of levels of maturity of atechnology from inception through to fully developed. The number ofmeasures of maturity can vary as desired, with each level describing adifferent level of maturity. Whereas the triangular distributions can beassigned to any of a number of different

TABLE 3 Risk/Return Distributions StdDev. Triangular Distributions Dist.LogStdDev. LogMean 97.5% 2.5% Max Min Mode 20% 0.19 0.04 1.50 0.72 1.500.72 1.00 30% 0.27 0.07 1.80 0.64 1.80 0.64 1.00 40% 0.33 0.11 2.13 0.582.13 0.58 1.00 50% 0.39 0.15 2.47 0.55 2.47 0.55 1.00 60% 0.43 0.19 2.610.52 2.61 0.52 1.00 70% 0.47 0.22 3.16 0.50 3.16 0.50 1.00 80% 0.51 0.263.51 0.48 3.51 0.48 1.00 90% 0.54 0.29 3.85 0.46 3.85 0.46 1.00 100% 0.67 0.32 4.20 0.45 4.20 0.45 1.00measures of maturity, in one advantageous embodiment, the triangulardistributions are assigned to NASA TRL's, as such are known to thoseskilled in the art and are described above in the background section.

The triangular distributions can be assigned to the measures of maturityin any of a number of different manners. In one advantageous embodiment,each percentage standard deviation of the risk/return distributions isassociated with an equal percentage uncertainty, or risk. For example,the percentage standard deviation of 20% from Table 2 above can beassociated with a 20% uncertainty, as such is shown in Table 1.Continuing, then, the percentage standard deviation of 30% from Table 2can be associated with a 30% uncertainty, as such is shown in Table 1,and so forth. Each uncertainty, in turn, can be associated with adifferent measure of maturity. As will be appreciated, typically themore mature a technology, the lower the risk. Therefore, eachuncertainty can be associated with a measure of maturity, with the lowerlevels of uncertainty associated with the higher measures of maturitydescribing higher levels of development. For example, consider the NASATRL's where level 1 corresponds to the lowest level of development, andlevels 2-9 corresponding to increasing levels of development. Taking thepercentage uncertainties associated with the percentage standarddeviations from Table 2, then, level 1 can be associated with thelargest uncertainty, that is, an uncertainty of 100%, while level 9 canbe associated with the smallest uncertainty, that is, an uncertainty of20%, and so forth.

With the measures of maturity associated with percentages of uncertaintythat have corresponding percentage standard deviations of therisk/return distributions, each measure of maturity can be associatedwith a risk/return distribution, and therefore, a triangulardistribution. Continuing the example of risk/return distributions andtriangular distributions from Tables 1 and 2, respectively, the measuresof maturity can be associated with the triangular distributions in amanner summarized in Table 4, below. As shown in Table 4, eachuncertainty is associated with a NASA TRL, as well as with a returnvalue, as such may be determined as described above. In turn, each NASATRL is associated with a triangular distribution, defined by a minimum,a maximum and a mode, with a max/min and mean also shown. It will benoted that each percentage of uncertainty is associated with atriangular distribution having a maximum and a minimum equal to thetruncated bounds of a risk/return distribution having a percentagestandard deviation equal to the percentage of uncertainty. Thus, 20%uncertainty is associated with a triangular distribution having amaximum and a minimum of 1.50 and 0.72, respectively, which equal the2.5% and 97.5% bounds of a risk/return distribution having a 20%standard deviation.

After associating the triangular distributions with the measures ofmaturity, the triangular distributions and associated measures ofmaturity can be utilized to model monetary measures associated with aparticular technology. With that, reference is now drawn to FIG. 4,which illustrates one embodiment of a method of determining a monetarydistribution, such as a cost or revenue distribution, associated with agood. As shown in block 22, the method begins by assigning a measure ofmaturity, and therefore a triangular distribution, to the technology, assuch may be assigned by a technologist or the like. Thereafter, as shownin block 24, a point cost (recurring or nonrecurring) or revenue value,or more typically a most likely cost or revenue value, associated withthe technology is defined or otherwise provided, such as by an estimatoror the like. As described below, the most likely cost or revenue valuewill thereafter be designated as the mode of a cost or revenuedistribution.

After assigning a triangular distribution and defining a most likelycost or

TABLE 4 Tech. Maturity Business Climate NASA Triangular DistributionsReturns Uncertainty TRL Max Min Mode 10.0% 20% 9 1.50 0.72 1.00 12.5%30% 8 1.80 0.64 1.00 15.0% 40% 7 2.13 0.58 1.00 17.5% 50% 6 2.47 0.551.00 20.0% 60% 5 2.61 0.52 1.00 22.5% 70% 4 3.16 0.50 1.00 25.0% 80% 33.51 0.48 1.00 27.5% 90% 2 3.85 0.46 1.00 30.0% 100%  1 4.20 0.45 1.00revenue, cost or revenue associated with the technology, and thereforethe good, can be modeled based upon a cost or revenue distribution. Inthis regard, the cost or revenue distribution can be determined, such asby mapping the most likely cost or revenue onto the respectivetriangular distribution, as shown in block 26. For example, the cost orrevenue distribution can be determined by determining the minimum andmaximum values, or more typically the truncated minimum and maximumvalues, that define the cost or revenue distribution. In this regard, asthe triangular distributions are typically normalized to a mode of one,the minimum and maximum values can be determined by simply multiplyingthe most likely cost or revenue by the minimum and maximum values of therespective triangular distribution. Thereafter, the cost or revenuedistribution can be defined by the minimum and maximum, as well as themost likely cost or revenue.

From the cost or revenue distribution, a cost or revenue can be selectedthat accounts for a qualitative measure of maturity of the respectivetechnology and, as such, the risks and returns associated with therespective technology, as shown in block 28. The cost or revenue can beselected in any of a number of different manners. For example, the costor revenue value can be selected according to a method for randomlyselecting the cost or revenue value, such as the Monte Carlo technique.As known to those skilled in the art, the Monte Carlo technique is amethod of randomly generating values for uncertain variables to simulatea model.

After selecting the cost or revenue value, a number of other cost orrevenue values can be selected, such as according to the Monte Carlotechnique, as shown in blocks 28 and 30. After selecting a number ofcost or revenue values, such as selecting N values, a sensitivitydistribution can be determined based upon the values, as shown in block32. In this regard, a sensitivity distribution can be determined bydetermining a mean and a standard deviation from the selected cost orrevenue values, and thereafter defining the sensitivity distributionbased upon the mean and standard deviation. The sensitivity distributioncan be defined to comprise any of a number of different types ofdistributions, but in one embodiment, the sensitivity distributioncomprises a lognormal distribution.

More particularly as to costs and revenues, then, when the valuesselected are recurring cost values, for example, a cost sensitivitydistribution can be determined from the cost values. Alternatively, forexample, when the values selected are revenue values, a pricesensitivity distribution can be determined from the revenue values. Inthis regard, the cost sensitivity distribution generally assigns aprobability of producing one unit of the good to each respectiverecurring cost at which manufacturers would produce the good. Similarly,the price sensitivity distribution generally assigns a probability of aunit purchase of the good to each respective price at which consumerswould purchase the unit. The price sensitivity distribution and/or thecost sensitivity distribution can then be used, such as to model demandand associated profitability of a good, such as is described in U.S.patent application Ser. No. 10/453,727 entitled: Systems, Methods andComputer Program Products for Modeling Demand, Supply and AssociatedProfitability of A Good, filed Jun. 3, 2003; and U.S. patent applicationSer. No. 10/453,779, entitled: Systems, Methods and Computer ProgramProducts for Determining A Learning Curve Value and Modeling anAssociated Profitability and Costs of A Good, filed Jun. 3, 2003, thecontents of both which are hereby incorporated by reference in theirentireties.

As will be appreciated, it is oftentimes desirable to determine whetherthe profitability of the good is positive before exercising a contingentclaim, such as whether to initiate or continue the project.Alternatively, it is desirable to determine whether the profitability ofthe good is above a predetermined threshold before exercising thecontingent claim. Contingent claims oftentimes come in the form of acall in which the manufacturer has an option to invest an amount ofmoney, or additional amounts of money, in order to start producing orcontinue producing the good. As such, if the initial stages of theproduction and sale of the good have proved unsuccessful and/or if thefuture prospects for the profitability of the good appear bleak, themanufacturer will likely decline to invest the money, or additionalmoney, and thereby forego exercise of the call and will thereforedecline to produce the good or terminate production of the good.Alternatively, if the initial stages of the production and sale of thegood have been successful and/or if the prospects of the profitabilityof the good are bright, the manufacturer will likely make the necessaryinvestment in order to begin or continue production of the good.

Regardless of the type of contingent claim, it is desirable to determinethe value of a good and, in particular, the contingent claim at thepresent time. By determining the value of the contingent claim, themanufacturer can avoid overpaying for production of the good as a resultof an overvaluation of the contingent claim. Conversely, themanufacturer can identify goods in which the value of the contingentclaim has been undervalued and can give strong consideration toinvesting in the production of these goods since they likely representworthwhile investment opportunities. As such, by accurately capturingthe cost and revenue risks, such as in determining the cost and pricesensitivity distributions, the demand and cost of a good and, thus, theprofitability of a good, can be accurately modeled. In this regard, thesystems, methods and computer program products of the present inventioncan facilitate determining the value of the good and, in particular, thecontingent claim at the present time. For more information ondetermining the value of the project, see U.S. patent application Ser.No. 09/902,021 entitled: Systems, Methods and Computer Program Productsfor Performing a Generalized Contingent Claim Valuation, the contents ofwhich are hereby incorporated by reference in its entirety.

As will be appreciated by those skilled in the art, many goods areassociated with more than one technology that may have different levelsof development, or measures of maturity. In such instances, a cost orrevenue distribution can be determined for each such technologyassociated with the good. For example, see Table 5, which illustrates agood having a total of twelve technologies utilized to bring the good tomarket, with each technology having an associated measure of maturityand a most likely recurring cost value. Then, from the triangulardistributions associated with the measures of maturity and the mostlikely recurring cost value, a cost or revenue distribution, such as arecurring cost distribution, can be determined for each technology. Inaddition, a total cost or revenue distribution can be associated withthe good based upon the sum of the most likely cost or revenue values ofthe individual technologies, as well as the sum of the minimum andmaximum values, respectively, of the individual technologies.

TABLE 5 Technology Cost/Risk Most Likely Estimates Maturity Cost LowHigh Configuration Technology L2 TRL 6 $100 $55 $247 EngineeringDevelopment L2 TRL 7 $200 $116 $426 Manufacturing L2 TRL 8 $400 $256$720 Integration/Support L2 TRL 9 $300 $216 $450 Node Cost/Risk InputsMaturity Point Est. Low High RecTechnology1 TRL 6 $600 $330 $1,482RecTechnology2 TRL 7 $400 $232 $852 RecTechnology3 TRL 5 $600 $312$1,686 RecTechnology4 TRL 7 $200 $116 $426 RecTechnology5 TRL 5 $600$312 $1,686 RecTechnology6 TRL 5 $400 $208 $1,124 RecTechnology7 TRL 7$100 $58 $213 RecTechnology8 TRL 5 $2,000 $1040 $5,620 Total RecurringCosts $5,900 $3,251 $14,932

To select a cost or revenue when the good is associated with a pluralityof technologies, a cost or revenue can be selected for each technologybased upon the respective cost or revenue distribution, such asaccording to the Monte Carlo technique. Thereafter, the individualselected costs or revenues can be summed into a total cost or revenuefor the good. Alternatively, a cost or revenue for the good can beselected, such as according to the Monte Carlo technique, based upon thetotal cost or revenue distribution, as such may be determined asdescribed above. Like before, after selecting the cost or revenue value,a number of other cost or revenue values can be selected, such asaccording to the Monte Carlo technique. Then, a sensitivitydistribution, such as a cost sensitivity or price distribution, can bedetermined based upon the values.

Therefore, embodiments of the present invention are capable of modelinga monetary measure of a good, such as a cost and/or revenue associatedwith a good, based upon technology maturity levels. In this regard,embodiments of the present invention can model quantitative risk/returnas a function of qualitative measures of maturity to thereby reliablyquantify the technical risk of a good based upon the level of maturityof the technologies associated with the good. Advantageously,embodiments of the present invention are capable of modeling thequantitative risk/return for technologies at a number of differentlevels of development, where each level of development has an associatedtechnical risk, or uncertainty. In addition, embodiments of the presentinvention are capable of modeling quantitative risk/return in a robustmanner such that monetary measures can be modeled in a number ofdifferent contexts, without modifying the model to fit a particularmonetary measure, good, project or the like.

As shown in FIG. 4, the system of the present invention is typicallyembodied by a processing element and an associated memory device, bothof which are commonly comprised by a computer 40 or the like. In thisregard, as indicated above, the method of embodiments of the presentinvention can be performed by the processing element manipulating datastored by the memory device with any one of a number of commerciallyavailable computer software programs. In one embodiment, the method canbe performed with data that is capable of being manipulated and/orpresented in spreadsheet form. For example, the method can be performedby the processing element manipulating data stored by the memory devicewith Excel, a spreadsheet software program distributed by the MicrosoftCorporation of Redmond, Wash., including Crystal Ball, a Monte Carlosimulation software program distributed by Decisioneering, Inc. ofDenver, Colo. The computer can include a display 42 for presentinginformation relative to performing embodiments of the method of thepresent invention, including the various distributions, models and/orconclusions as determined according to embodiments of the presentinvention. To plot information relative to performing embodiments of themethod of the present invention, the computer can further include aprinter 44.

Also, the computer 40 can include a means for locally or remotelytransferring the information relative to performing embodiments of themethod of the present invention. For example, the computer can include afacsimile machine 46 for transmitting information to other facsimilemachines, computers or the like. Additionally, or alternatively, thecomputer can include a modem 48 to transfer information to othercomputers or the like. Further, the computer can include an interface(not shown) to a network, such as a local area network (LAN), and/or awide area network (WAN). For example, the computer can include anEthernet Personal Computer Memory Card International Association(PCMCIA) card configured to transmit and receive information to and froma LAN, WAN or the like.

In one advantageous technique applicable to embodiments of the presentinvention, the methods according to embodiments of the present inventionmay be embodied in a software or data module, component, portfolio orthe like, that can be manipulated or otherwise operated within aspreadsheet software program such as Excel. Such a technique may beadvantageous in a number of different contexts, such as in the contextof financial modeling and analysis. In this regard, modules, componentsand/or a portfolio that perform various financial modeling functions canbe combined to gain a more complete understanding of a financialcontext. A brief description of such a technique as such may be appliedto the present invention will now be described below.

According to such a technique, data capable of being manipulated toperform at least a portion of the methods of the present invention canbe embodied in a module, which can thereafter be linked or otherwiseassociated with other portions of the methods of the present inventionembodied in other modules so as to formulate a component. Then, if sodesired, the component can be linked or otherwise associated with othercomponents capable of performing other related methods to thereby form aportfolio. For example, methods of determining the price sensitivitydistribution according to embodiments of the present invention can beembodied in one module. The module can then be linked or otherwiseassociated with another module for modeling demand such that the demandmodule can model demand based upon the price sensitivity distributiondetermined by the price sensitivity module. The demand module can thenbe linked or otherwise associated with a module for modeling cost tothereby formulate a component capable of modeling profitability basedupon the demand and cost models. Then, if so desired, the component formodeling profitability can be linked or otherwise associated withanother component to perform another function. For example, thecomponent for modeling profitability can be linked or otherwiseassociated with a component capable of forecasting revenue over time tothereby create a business case for the good. In this regard, such acomponent capable of forecasting revenue over time may operate accordingto U.S. patent application Ser. No. 10/453,396, entitled: Systems,Methods and Computer Program Products for Modeling Uncertain FutureBenefits.

According to one aspect of the present invention, the system of thepresent invention generally operates under control of a computer programproduct according to another aspect of the present invention. Thecomputer program product for performing the methods of embodiments ofthe present invention includes a computer-readable storage medium, suchas the non-volatile storage medium, and computer-readable program codeportions, such as a series of computer instructions, embodied in thecomputer-readable storage medium.

In this regard, FIGS. 1 and 3 are a flowchart of methods, systems andprogram products according to the invention. It will be understood thateach block or step of the flowchart, and combinations of blocks in theflowchart, can be implemented by computer program instructions. Thesecomputer program instructions may be loaded onto a computer or otherprogrammable apparatus to produce a machine, such that the instructionswhich execute on the computer or other programmable apparatus createmeans for implementing the functions specified in the flowchart block(s)or step(s). These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable apparatus to function in a particular manner, such that theinstructions stored in the computer-readable memory produce an articleof manufacture including instruction means which implement the functionspecified in the flowchart block(s) or step(s). The computer programinstructions may also be loaded onto a computer or other programmableapparatus to cause a series of operational steps to be performed on thecomputer or other programmable apparatus to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide steps for implementingthe functions specified in the flowchart block(s) or step(s).

Accordingly, blocks or steps of the flowchart support combinations ofmeans for performing the specified functions, combinations of steps forperforming the specified functions and program instruction means forperforming the specified functions. It will also be understood that eachblock or step of the flowchart, and combinations of blocks or steps inthe flowchart, can be implemented by special purpose hardware-basedcomputer systems which perform the specified functions or steps, orcombinations of special purpose hardware and computer instructions.

Many modifications and other embodiments of the invention will come tomind to one skilled in the art to which this invention pertains havingthe benefit of the teachings presented in the foregoing descriptions andthe associated drawings. Therefore, it is to be understood that theinvention is not to be limited to the specific embodiments disclosed andthat modifications and other embodiments are intended to be includedwithin the scope of the appended claims. Although specific terms areemployed herein, they are used in a generic and descriptive sense onlyand not for purposes of limitation.

1. A method of modeling a monetary measure of a good, the method beingperformed by execution of computer-readable program code by at least oneprocessor of at least one computer system, the method comprising:selecting, using at least one of the processors, at least onequalitative measure of maturity for at least one technology associatedwith the good, wherein each qualitative measure of maturity isassociated with a distribution such that each technology is associatedwith the distribution of the respective qualitative measure of maturity;defining, using at least one of the processors, a monetary pointassociated with each technology; determining, using at least one of theprocessors, a monetary distribution for each technology based upon therespective monetary point and the respective distribution; selecting,using at least one of the processors, a plurality of monetary valuesincluding randomly selecting the plurality of monetary values for eachtechnology based upon the respective monetary distribution; andgenerating, using at least one of the processors, a model of themonetary measure for the good based upon the selected monetary valuesfor each technology.
 2. A method according to claim 1, wherein selectingat least one qualitative measure comprises selecting at least onequalitative measure from a model of quantitative risk or return.
 3. Amethod according to claim 2 further comprising generating a model ofquantitative risk or return, including: determining a distribution foreach of a plurality of risk values, wherein the plurality of risk valuesare ordered from a lowest risk value to a highest risk value; andassigning a qualitative measure of maturity to each of the plurality ofdistributions based upon a respective risk value to thereby generate amodel of quantitative risk or return as a function of the plurality ofmeasures of maturity, wherein the measures of maturity are ordered froma highest measure of maturity to a lowest measure of maturity, andwherein assigning the plurality of measures of maturity comprisesassigning a higher measure of maturity to a distribution associated witha lower risk value.
 4. A method according to claim 1, wherein defining amonetary point comprises defining a cost point, wherein determining amonetary distribution comprises determining a cost distribution for eachtechnology based upon a respective cost point and the respectivedistribution, wherein selecting a plurality of monetary values comprisesselecting a plurality of cost values, and wherein generating a model ofa monetary measure comprises determining a cost sensitivity distributionbased upon the selected cost values for each technology to therebygenerate a model of the cost of producing the good.
 5. A methodaccording to claim 1, wherein defining a monetary point comprisesdefining a revenue point, wherein determining a monetary distributioncomprises determining a revenue distribution for each technology basedupon the respective revenue point and the respective distribution,wherein selecting a plurality of monetary values comprises selecting aplurality of revenue values, and wherein generating a model of amonetary measure comprises determining a price sensitivity distributionbased upon the selected revenue values for each technology to therebygenerate a model of the revenue associated with the good.
 6. Anapparatus for modeling a monetary measure of a good comprising: aprocessor configured to select or receive selection of at least onequalitative measure of maturity for at least one technology associatedwith the good, wherein each qualitative measure of maturity isassociated with a distribution such that each technology is associatedwith the distribution of the respective qualitative measure of maturity,wherein the processor is also configured to define a monetary pointassociated with each technology, and thereafter determine a monetarydistribution for each technology based upon the respective monetarypoint and the respective distribution, wherein the processor isadditionally configured to select a plurality of monetary valuesincluding being configured to randomly select the plurality of monetaryvalues for each technology based upon the respective monetarydistribution, and wherein the processor is further configured togenerate a model of the monetary measure for the good based upon theselected monetary values for each technology.
 7. An apparatus accordingto claim 6, wherein the processor being configured to select or receiveselection of at least one qualitative measure includes being configuredto select or receive selection of at least one qualitative measure froma model of quantitative risk or return.
 8. An apparatus according toclaim 7, wherein the processor is also configured to generate a model ofquantitative risk or return, including being configured to: determine adistribution for each of a plurality of risk values, wherein theplurality of risk values are ordered from a lowest risk value to ahighest risk value; and assign a qualitative measure of maturity to eachof the plurality of distributions based upon a respective risk value tothereby generate a model of quantitative risk or return as a function ofthe plurality of measures of maturity, wherein the measures of maturityare ordered from a highest measure of maturity to a lowest measure ofmaturity, and wherein the processor being configured to assign aqualitative measure of maturity to each of the plurality ofdistributions includes being configured to assign a higher measure ofmaturity to a distribution associated with a lower risk value.
 9. Anapparatus according to claim 6, wherein the processor being configuredto define a monetary point includes being configured to define a costpoint, wherein the processor being configured to determine a monetarydistribution includes being configured to determine a cost distributionfor each technology based upon the respective cost point and therespective distribution, wherein the processor being configured toselect a plurality of monetary values includes being configured toselect a plurality of cost values, and wherein the processor beingconfigured to generate a model of a monetary measure includes beingconfigured to determine a cost sensitivity distribution based upon theselected cost values for each technology to thereby generate a model ofthe cost of producing the good.
 10. An apparatus according to claim 6,wherein the processor being configured to define a monetary pointincludes being configured to define a revenue point, wherein theprocessor being configured to determine a monetary distribution includesbeing configured to determine a revenue distribution for each technologybased upon the respective revenue point and the respective distribution,wherein the processor being configured to select a plurality of monetaryvalues includes being configured to select a plurality of revenuevalues, and wherein the processor being configured to generate a modelof a monetary measure includes being configured to determine a pricesensitivity distribution based upon the selected revenue values for eachtechnology to thereby generate a model of the revenue associated withthe good.
 11. A computer program product for modeling a monetary measureof a good, the computer program product comprising a computer-readablestorage medium having computer-readable program code portions storedtherein, the computer-readable program portions comprising: a firstexecutable portion configured to select or receive selection of at leastone qualitative measure of maturity for at least one technologyassociated with the good, wherein each qualitative measure of maturityis associated with a distribution such that each technology isassociated with the distribution of the respective qualitative measureof maturity; a second executable portion configured to define a monetarypoint associated with each technology; a third executable portionconfigured to determine a monetary distribution for each technologybased upon the respective monetary point and the respectivedistribution; a forth executable portion configured to select aplurality of monetary values by randomly selecting the plurality ofmonetary values for each technology based upon the respective monetarydistribution; and a fifth executable portion configured to generate amodel of the monetary measure for the good based upon the selectedmonetary values for each technology.
 12. A computer program productaccording to claim 11, wherein the forth executable portion beingconfigured to select or receive selection of at least one qualitativemeasure of maturity includes being configured to select at least onequalitative measure from a model of quantitative risk or return.
 13. Acomputer program product according to claim 12, wherein thecomputer-readable program portions further comprise a sixth executableportion configured to generate a model of quantitative risk or return,including being configured to: determine a distribution for each of aplurality of risk values, wherein the plurality of risk values areordered from a lowest risk value to a highest risk value; and assign aqualitative measure of maturity to each of the plurality ofdistributions based upon a respective risk value to thereby generate amodel of quantitative risk or return as a function of the plurality ofmeasures of maturity, wherein the measures of maturity are ordered froma highest measure of maturity to a lowest measure of maturity, andwherein the sixth executable portion being configured to assign aqualitative measure of maturity to each of the plurality ofdistributions includes being configured to assign a higher measure ofmaturity to a distribution associated with a lower risk value.
 14. Acomputer program product according to claim 11, wherein the secondexecutable portion being configured to define a monetary point includesbeing configured to define a cost point, wherein the third executableportion being configured to determine a monetary distribution for eachtechnology includes being configured to determine a cost distributionfor each technology based upon the respective cost point and therespective distribution, wherein the forth executable portion beingconfigured to select a plurality of monetary values includes beingconfigured to select a plurality of cost values, and wherein the fifthexecutable portion being configured to generate a model of the monetarymeasure includes being configured to determine a cost sensitivitydistribution based upon the selected cost values for each technology tothereby generate a model of the cost of producing the good.
 15. Acomputer program product according to claim 11, wherein the secondexecutable portion being configured to define a monetary point includesbeing configured to define a revenue point, wherein the third executableportion being configured to determine a monetary distribution for eachtechnology includes being configured to determine a revenue distributionfor each technology based upon the respective revenue point and therespective distribution, wherein the forth executable portion beingconfigured to select a plurality of monetary values includes beingconfigured to select a plurality of revenue values, and wherein thefifth executable portion being configured to generate a model of themonetary measure includes being configured to determine a pricesensitivity distribution based upon the selected revenue values for eachtechnology to thereby generate a model of the revenue associated withthe good.